TORRCH: Tomographic reconstruction of the reionization of cosmic hydrogen with Ly${\alpha}$ emitters and non-Ly${\alpha}$-selected galaxies

TL;DR

This paper introduces TORRCH, a deep-learning framework that reconstructs the ionization topology of cosmic hydrogen during reionization using galaxy distributions, enabling direct field-level insights beyond traditional summary statistics.

Contribution

TORRCH is the first deep-learning method to perform tomographic reconstruction of reionization fields from galaxy survey data, capturing large-scale morphology and ionization features.

Findings

Accurately recovers large-scale ionization morphology from mock surveys.

Reproduces the one-point distribution and power spectrum of neutral fractions.

Effectively captures galaxy-IGM cross-correlation features.

Abstract

Tomographic reconstruction of reionization is a long-sought goal. It would move the field beyond global summary statistics, such as the volume-averaged ionised fraction, to direct, field-level constraints on the ionization topology. With this in mind, we present TORRCH (TOmographic Reconstruction of the Reionization of Cosmic Hydrogen), a deep-learning framework that reconstructs the neutral-hydrogen fraction field during the epoch of reionization from the spatial distributions of Ly$\alpha$ emitters (LAEs) and non-Ly$\alpha$-selected galaxies (NLSGs) at luminosity limits comparable to current surveys. Using hydrodynamical simulations post-processed with radiative transfer, we train a deterministic 3D U-Net on mock surveys spanning diverse reionization scenarios and predict the neutral-fraction field. We find that TORRCH recovers the large-scale ionization morphology from synthetic data comparable to current surveys with high fidelity, and reproduces both the one-point distribution and the 2D power spectrum of projected neutral fractions. The predicted galaxy-IGM cross-correlation is also captured well, including the expected small-scale anti-correlation and its decline towards zero at large separations. Reconstruction quality depends on tracer completeness, with deep joint LAE+NLSG samples yielding the most accurate morphology, while LAE-only selections retain bubble-scale topology but with reduced fidelity. Robustness tests show that the method is stable to variations in ionization conditions between training and test data, and to realistic redshift uncertainties. Our results suggest that galaxy-based tomography can potentially deliver reliable reionization maps across realistic survey redshift windows.